Tourism Management 34 (2013) 71e79
Contents lists available at SciVerse ScienceDirect
Tourism Management
journal homepage: www.elsevier.com/locate/tourman
Being better vs. being different: Differentiation, competition, and pricing
strategies in the Spanish hotel industry
Manuel Becerra*, Juan Santaló, Rosario Silva
IE Business School, Alvarez de Baena 4, 28006 Madrid, Spain
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 4 October 2011
Accepted 23 March 2012
We study the effects of vertical and horizontal differentiation on pricing policy in a large sample of hotels
in Spain. We show that hotels with more stars (i.e., vertically differentiated) offer smaller discounts over
listed prices, in addition to charging higher prices. Similarly, hotels that belong to a branded chain (i.e.,
horizontal differentiation) also charge higher prices and provide smaller discounts. We show how the
degree of local competition moderates the effect of differentiation on pricing policy, but only for vertical
differentiation. Differentiation indeed protects hotels from the pressure to reduce prices as competition
increases, but being better seems to be more effective than just being different.
Ó 2012 Elsevier Ltd. All rights reserved.
Keywords:
Differentiation
Competition
Prices
Hotel industry
1. Introduction
Product differentiation has been widely regarded as one way for
firms to isolate themselves from the pressure of competitors and
thus obtain superior performance (Bain, 1956; Dickson & Ginter,
1987; Porter, 1980). Defining long ago this critical concept in firm
strategy, prominent economist Edward Chamberlin noted that “A
general class of product is differentiated if any significant basis exists
for distinguishing the goods of one seller from those of another and
leads to a preference for one variety of the product over another”
(Chamberlin, 1933: 56). However, despite its importance, there is
only scarce empirical research that investigates which types of
product differentiation isolate firms more effectively from competitive forces (Porter, 1980). In this paper we try to fill this gap by
analyzing the effect of two distinct differentiation strategies, vertical
and horizontal, on pricing policies in the hotel industry.
Several empirical studies in the hotel industry have documented
the benefits of differentiation (Baum & Mezias, 1992; GarrigósSimón & Palacios Marqués, 2004), which can be viewed as
a barrier to entry or, more generally, a generic source of competitive
advantage in sharp contrast to cost leadership. Some authors
believe that all competitive strategies, including cost leadership,
involve a certain differentiation of the firm’s products and services
versus its competitors in some way (Mintzberg, 1988). Extant
research has focused primarily on the contrast between cost leadership and differentiation, usually exploring whether they do exist
* Corresponding author. Tel.: þ34 91 568 9600.
E-mail addresses: manuel.becerra@ie.edu (M. Becerra), juan.santalo@ie.edu
(J. Santaló), charo.silva@ie.edu (R. Silva).
0261-5177/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved.
doi:10.1016/j.tourman.2012.03.014
as alternative choices for firm strategy and their implications for
performance (Campbell-Hunt, 2000). The positive effects of
differentiation in protecting firms from competition have been
widely accepted since Porter (1980), but the conditions under
which they do so have not been investigated empirically yet.
We study the isolating effect of differentiation strategies from
competitive pressures in the context of the Spanish hotel industry.
According to the World Tourism Organization, Spain was one of the
main destinations in the world in 2010 in terms of arrivals and the
second in international tourism receipts after the US (World
Tourism Organization, 2011). Tourism’s contribution to Spanish
gross domestic product (GDP) is estimated at 10%, whereas
worldwide is around 5%. Similarly, tourism’s contribution to
Spanish employment represents 10.8% of the overall number of
jobs, while worldwide is estimated in order of 6e7% (Instituto de
Estudios Turísticos, 2011; World Tourism Organization, 2011). The
Spanish hotel industry is a particularly appropriate context to
conduct this study because it provides a rich setting across a large
number of geographical locations with varying levels of competitive rivalry for which there are publicly available statistics of listed
prices, discounts, and features for hotel differentiation.
Looking at the extant literature, our knowledge is still limited
with regard to how much the options for differentiation can isolate
hotels from the negative effects of competition on prices. Though
the effect of specific hotel features on room prices has been the
subject of abundant empirical research (Bull, 1994; Espinet, Saez,
Coenders, & Fluvià, 2003; Haroutunian, Mitsis, & Pashardes, 2005;
Rigall-I-Torrent & Fluvià, 2011; Thrane, 2005), we do not know the
extent to which alternative types of differentiation can protect
hotels from the pressure to reduce prices when competition in their
72
M. Becerra et al. / Tourism Management 34 (2013) 71e79
geographical area increases. Based on the differentiation literature
in IO (Beath & Katsoulacos, 1991), we investigate how vertical
differentiation (i.e., competing along one product dimension valued
similarly by all customers, such as overall hotel quality) and horizontal differentiation (i.e., offering a unique combination of product
features that satisfies the needs of a specific customer segment) can
both be used to insulate hotels from an increase in the degree of
competition in its location. In addition to finding support for the
effects of both vertical and horizontal differentiation on room prices and discounts, our empirical results show that “being better”
(i.e., vertically differentiated) allows hotels to resort less to room
price discounts from their listed prices as competition increases
than merely “being different” (horizontally differentiated).
In the rest of the paper we develop several hypotheses about the
effects of vertical and horizontal differentiation on hotel room
prices and how competitive rivalry moderates these effects. We use
a sample of 1490 hotels in 67 locations in Spain to test the
hypotheses, which are generally supported.
2. Differentiation strategies
2.1. Literature review
The concept of differentiation goes back to the seminal work on
monopolistic competition of Chamberlin (1933), who highlighted
that customers may have different preferences among available
products within the same industry. Along this line, Porter (1980)
later popularized the generic strategy of differentiation when
a firm creates something tangible or intangible that is perceived as
“being unique” by at least one set of customers. Thus, it is the
customers’ perceptions what determines the extent of product
differentiation.
Differentiation has been regarded as an important generic
strategy widely used across all industries (Beal & Yasai-Ardekani,
2000; Homburg, Krohner & Workman, 1999), but their performance consequences are not well understood yet (Campbell-Hunt,
2000). Furthermore, there seem to be many possible differentiation
strategies. For instance, Miller (1986) argued in favor of two types
of differentiation strategy: innovation and marketing, which was
supported by Lee and Miller (1999). In a broader categorization of
differentiation-based strategies, Mintzberg (1988) proposed six
types: quality, design, support, image, price, and undifferentiated
products, which obtained empirical support from Kotha and
Vadlamani (1995). Recently, strategy researchers have explored
the distinction between vertical and horizontal differentiation
widely used in the IO literature (Ethiraj & Zhu, 2008; Makadok,
2010, 2011).
In the case of vertical differentiation all customers would agree
in a preference ranking of available products, if they were offered at
the same price. In this case, competition among firms takes place
along only one dimension with the most differentiated firm
providing the highest level of such a dimension. For instance, by
offering the highest overall quality, a hotel may become more
attractive to all customers. In this case, even though customers have
the same ranking of perceived product quality, products sell at
different prices because customers have different willingness to
pay for quality improvements, driven primarily by their differences
in wealth (Beath & Katsoulacos, 1991).
However, customers often have different preferences about the
set of desirable features in a product or service, so that a single
ranking along a quality index cannot be developed for the firms in
the market for which all customers would agree. This is the case of
horizontal differentiation in which, even if all products were sold at
the same price, firms would obtain different market shares to the
extent that their products have a unique combination of attributes
that are preferred by one specific set of customers. For instance,
through brand loyalty a firm becomes more attractive to a specific
set of customers with similar needs, which limits the degree of
substitutability among competing firms (Makadok, 2010).
Most of the IO literature on product differentiation is based on
this distinction between vertical and horizontal differentiation
(Beath & Katsoulacos, 1991). However, strategy research has
ignored this distinction until recently (Makadok & Ross, 2009). In
a sequence of theoretical papers, Makadok and Ross (2009),
Makadok (2010, 2011) predicted the performance consequences of
both types of differentiation as well as their interaction effects,
though there is scarce empirical research in strategy about both
types of differentiation. In one of the few empirical studies on
differentiation strategies, Ethiraj and Zhu (2008) show that
competition is primarily based on horizontal differentiation in the
early stages of industry development, which makes incumbents’
advantage relatively sustainable; however, as the industry
matures, new entrants with greater vertical differentiation are
more likely to beat incumbents.
In the next section we will explain how the two main alternatives for differentiation, vertical and horizontal strategies, can be
used to help us understand competition among hotels and its effect
on prices; more precisely, how hotels can cope with the pressure to
reduce room prices as the intensity of competition increases.
2.2. Differentiation and prices in the hotel industry
Let us investigate now the connection between differentiation
strategies and pricing policies in the specific framework of the
hospitality sector. This topic is worthwhile studying because price
planning is one of the most overlooked and poorly researched areas
of marketing (Hoffman, Turley, & Kelley, 2002; Rowley, 1997).
Furthermore, pricing decisions are particularly relevant in the hotel
industry since hospitality prices are one of the main influences on
accommodation selection decisions (Hung, Shang, & Wang, 2010;
Lockyer, 2005).
The existing literature in the hotel industry indicates that there
are many attributes that influence the customers’ choice, such as
location, room rate, service quality, reputation, security, and
cleanliness (Chu & Choi, 2000). Similarly, hedonic pricing research
has explored a large number of variables that determine room
prices, such as location, hotel category and size, brand name,
restaurant availability, distance to city center, room features,
parking, and sport facilities (Bull, 1994; Carvell & Herrin, 1990;
Espinet et al., 2003; Thrane, 2005; Wu, 1999). Any unique feature
that is relevant for at least one set of customers is a potential source
of differentiation (Dubé & Renaghan, 2000).
Certain hotel characteristics, such as service quality, are arguably important for all customers, but some customers value certain
features more than others. From the many possible features on
which hotels can build their differentiation, we have chosen two
important elements of a hotel’s strategy to conduct our study: hotel
category (i.e., number of stars) and hotel chain (i.e., membership to
a branded hotel chain), as critical choices for vertical and horizontal
differentiation respectively.
In the hotel industry there is one overall ranking of quality with
which we would expect all customers to substantially agree, i.e.,
hotel category (1 through 5 stars, which is actually assessed officially in Spain by the proper agency). In other words, most people
would agree that a five-star hotel is usually better than a four-star
hotel and so on. It is reasonable to believe that, if customers were
given the same rates for hotels, they would choose a higher category hotel. Thus, hotel category is an option for hotels to differentiate vertically and, indeed, it is regarded as an excellent proxy for
overall hotel quality (Fernández & Marín, 1998). Of course,
M. Becerra et al. / Tourism Management 34 (2013) 71e79
customers do not necessarily choose hotels with a greater number
of stars because hotels charge different prices and they may not be
willing to pay extra for higher quality since their wealth differs.
Obviously, we would expect a strong positive correlation
between hotel quality and room prices, which has been well
documented in the extant literature (Bull, 1994; Fernández & Marín,
1998; Israeli, 2002; Rigall-I-Torrent & Fluvià, 2011). Its economic
rationale is straightforward and not controversial, because higher
product quality is associated with higher costs, which pushes prices
up. A differentiator faces a downward-sloping demand curve,
which allows the firm to limit supply to only the less price sensitive
customers, thus, setting a larger price and usually getting greater
margin. Indeed, the existing empirical literature on differentiation
across different industries has shown that differentiation is
generally associated with higher prices (Caves & Williamson, 1985;
Mazzeo, 2002) and greater market power (Bresnahan, 1987;
Dranove, Gron, & Mazzeo, 2003).
However, we want to go one step beyond and analyze the effect
of hotel quality on discounts over listed prices (see Rowley, 1997,
for discounts as pricing policies). In any industry, market prices
are determined by a complex set of supply and demand factors. If
we want to study how differentiation protects hotels from
reducing their prices as competition increases, we should also use
discounts over listed prices in addition to average room prices.
Hotels usually set their prices for a double room as the reference
listed price over which they may apply a variety of discounts, for
instance, for single use of the room. In contrast to the obvious
quality-price relationship in room prices, it is far from evident
whether high or low quality hotels provide greater discounts for
a single room.
On the one hand, because less differentiated hotels compete
more heavily on prices (Hung, Shan & Wang, 2010; Israeli, 2002;
Rowley, 1997), they should have less cushion to provide additional
discounts over their already low listed prices. To the contrary, if
differentiation actually protects the firm from the pressure to
reduce prices (Porter, 1980; Rowley, 1997), we would expect that
hotels with more stars should provide less discounts.
We believe that prices as well as discounts should be driven by
similar determinants, including especially the extent of differentiation. In the preliminary interviews with hotel managers that we
conducted in the early stages of this project, several managers
indicated that they use specific discounts when the competitive
pressure increases, but they eventually have to lower listed prices if
the discounts were not sufficient to attract customers. Thus, we
argue that the positive effect of differentiation on prices should also
have an analogous effect in reducing the discounts offered to
specific customers groups. Based on these arguments, we can
formulate the following hypothesis regarding hotel category as
a strategic choice for vertical differentiation:
Hypothesis 1. Higher category hotels (a) charge higher prices for
double rooms and (b) provide smaller discounts for single rooms.
Turning now to horizontal differentiation, the benefits of large
branded chains over independent hotels have been already
explored in the literature (Holverson & Revaz, 2006). Empirical
research has documented the higher survival chances of hotels that
belong to a chain, identifying the chain reputation as one of the
important advantages that these hotels receive (Ingram & Baum,
1997). Brand name has also been identified as one of the main
hotel attributes that drive customer purchase decision because
customers value the familiarity with the brand and its positive and
distinctive image (Dubé & Renaghan, 2000). In this sense,
customers that are familiar with a given chain are more likely that
they stay loyal to it rather than take risks with an unfamiliar brand.
Thus, other things being held equal, brand hotels will be attractive
73
for at least some customers, those who consider that the brand is
more likely to satisfy their own needs.
It seems reasonable that the set of customers that have a preference for a specific chain brand should push its room prices up. In
fact, empirical research has shown a positive relationship between
chain affiliation and room prices (Thrane, 2007). Hotels that are
part of a chain may be expected to have somewhat higher prices
than independent hotels, which in general have a less well-known
brand behind them, even though the preferences toward specific
brands vary across customers. Note that since hotel brands cannot
be ranked in a strict order of preference valid for all customers,
branding constitutes an example of horizontal differentiation.
More importantly, we also expect that hotels differentiated
horizontally through a chain brand should be less likely to use
discounts, just like in the case of vertical differentiation. As Porter
noted, “Differentiation provides insulation against competitive
rivalry because of brand loyalty by customers and resulting lower
sensitivity to price.” (Porter, 1980: 38). To the extent that a chain
brand defends its hotels from the competitive pressure on prices, it
should also affect the discounts that they provide to attract specific
customers (Hanks, Cross, & Noland, 2002). Thus, we expect that
hotels that belong to a branded chain will try and take advantage of
the higher presumed loyalty of their customers and, consequently,
they should provide lower discounts over their listed prices for
double rooms. Thus, we can formulate the following hypothesis
with regard to chain brand as a choice for horizontal
differentiation:
Hypothesis 2. Hotels that belong to a branded chain (a) charge
higher prices for double rooms and (b) provide smaller discounts
for single rooms.
The IO literature has shown theoretically and empirically that an
increase in the intensity of market competition translates into
lower prices (Bresnahan & Reiss, 1991; Shaked & Sutton, 1982). We
will argue that both vertical and horizontal differentiation should
reduce the effect of competitive rivalry in prices and discounts. In
other words, the degree of competition moderates the effect of
differentiation in pricing policy, such that vertical and horizontal
differentiation protects hotels from the pressure to reduce prices
when competition increases.
As discussed earlier, vertical and horizontal differentiation are
essentially tools for the firm to escape from competitive rivalry,
which in the hotel industry is primarily driven by the number of
direct competitors and the geographical distance to them (Baum &
Mezias, 1992). Differentiated hotels try to attract a certain clientele,
while undifferentiated ones, whose main feature is low prices,
place their emphasis on operating at full capacity to have minimum
operation average costs. We expect that these two types of firms
should react differently to an increase in rivalry.
Differentiated hotels cannot react to an increase in competition
with greater discounts because these discounts could jeopardize
the value of intangibles, like quality reputation and chain brand,
which are likely to be linked to room prices in the mind of
customers. Lower prices and large discounts may destroy the image
of uniqueness of hotels with higher category and branded chains. In
contrast, non-differentiated hotels are more likely to use pricing
policy to attract customers when competition increases, that is,
when they have lower quality and are not part of a chain. Thus, we
expect that the positive effects of differentiation on pricing policy
hypothesized earlier will be stronger as competition increases.
Hypothesis 3. The effect of both hotel category and chain brand
on pricing policy becomes greater as the level of local competition
increases.
Fig. 1 summarizes the theoretical arguments developed above
that we will test empirically in the next sections.
74
M. Becerra et al. / Tourism Management 34 (2013) 71e79
Double Room
Price
Hotel Differentiation
- H1: Category
- H2: Chain
Discount for
Single Room
H3: Competition among Hotels
- Number of Hotels
- Geographical Distance (reversed)
Fig. 1. Model of hotel differentiation and competition.
3. Methodology
3.1. Data collection
Our final sample consists in 1490 hotels in 67 distinct destinations all located in Spain. We used several sources of information to
build a database of hotels for year 2005, including the Guía Oficial
de Hoteles (published by Instituto de Estudios Turísticos) for hotel
information, the Encuesta de Ocupación Hotelera (published by
Instituto Nacional de Estadística, INE) for location information, and
VisualMap for geographical distances among hotels within their
location. From the entire population of hotels in Spain, we selected
those located in the 135 locations for which the INE gathers visitor
information, which account for 70% of all visitors and 56% of
existing hotels. Thus, the initial sample consisted of 3456 hotels.
After discarding hotels with missing data (often due to the
impossibility to compute geographical distances to other hotels)
and those locations with less than five hotels, the final sample was
reduced to 1490 hotels in 67 locations. This final sample includes
the most important Spanish cities that attract national and international visitors, such as Madrid, Barcelona, Valencia, Sevilla, the
touristic centers in the Mediterranean and the Atlantic coast, and
the Canary and Balearic Islands.
The Guía Oficial de Hoteles gathers annual information about all
hotels in Spain, including its name, category (1e5 stars), chain
membership, location (city and address), number of rooms, prices
for double and single rooms in peak and off-peak season, and other
hotel features (e.g., age, picturesque location, and being close to the
beach). Hotels send this information to the public Spanish agency
Instituto de Estudios Turísticos, which is in charge of the promotion
of tourism in Spain, both domestically and internationally. Hotel
information, including room prices, comes from this guide, which is
often used by researchers in the Spanish hotel industry (Fernández
& Marín, 1998; Uriel & Ferri, 2004). The Guía is widely available to
the general public, who often use it for planning their trips and
choosing a hotel. To check the validity of the pricing data in the
Guía, we manually collected hotel prices advertised by two tour
operators on a random sample of 70 hotels and found a high
correlation of .72 with those in the Guía, though tour operator
prices were on average 11% lower. Overall, we were reasonably
reassured of the reliability of the pricing data.
3.2. Variables
We use the price of a double room during peak season as the
basis for the dependent variables in the analysis (Room Price), as
reported by the Guía. Hotels also report separately the price of
a single room, so that we could compute the percentage discount of
a single room over the price of a double room (Price Discount). Over
90% of the hotel rooms in the sample were double, but they can be
rented by single individuals usually at a discount over the listed
price (single occupancy of a double room). It should be noted that in
two observations it was cheaper for the visitor the possibility of
renting two single rooms than getting one double room and we
dropped these two observations for the Price Discount analysis.
The key independent variables were operationalized in the
following way:
- Category. The number of stars (coded 1 though 5) of the hotel is
reported by the Guía Oficial de Hoteles. As a measure of vertical
differentiation, this variable captures objectively the level of
quality of the hotel based on regional regulations, which is
officially assessed by the proper agencies and displayed
prominently.
- Chain. This is a dummy variable that represents whether the
hotel is part of a chain as shown in the Guía (coded 1), such as
NH, Barceló, and Sol-Meliá. It is a measure of horizontal
differentiation that denotes the use of a common umbrella
brand by the hotel. In contrast to vertical differentiation, the
potential value of any single brand varies across customers
based on their own experiences and preferences, so that no
objective ranking of brands may be developed.
- Local competition. We used two variables that capture the
degree of direct competition that a hotel has to face in its local
market. First, Number of Competitors is a straightforward
measure of the number of hotels in the same category (i.e.,
same number of stars) that exist in each one of the 67 locations
in the sample. Second, we computed the average Geographical
Distance to these direct competitors in each location. We
manually input the physical address of the hotels in VisualMap
software to estimate the geographical distance based on their
GPS coordinates between each hotel and all other hotels in its
location with the same category. Because the 67 locations in
the sample covered geographical areas of different size, we
normalized the distance to the farthest hotel in the location to
a value of 1 and, then, computed the average Euclidean
distance to other hotels.
In addition to the independent variables above, we also included
the following control variables:
- Beach Hotel. This dummy variable was coded as 1 when the
Guía indicates that the hotel is located near the beach. This is
a variable self-reported by the hotel that has intra-location
variation. In other words, inside a given location some hotels
are beach hotels while others located further inland are not. For
instance, there are hotels in Marbella that are located directly
on the beach as the Gran Hotel Don Pepe, while others are
placed several kilometers far from the beach as Rio Real Golf
Hotel or Incosol Hotel Medical Spa. We included this control
variable because beach hotels are quite different with respect
to their strategy and pricing policy.
- Mountain. This dummy variable was coded as 1 when the Guía
indicates that the hotel is located near a mountain area. Similar
to the previous variable, this variable is self-reported by the
hotel and it has intra-location variation. For instance, only
some hotels located in Ronda (Málaga) report to be located
near the mountain areas, as the Natural Park of Sierra de las
Nieves or the mountain range of Grazalema.
- Special. This dummy variable was coded as 1 when the Guía
indicates that the hotel is located near a special picturesque
spot, such as Madrid’s Museo del Prado or Barcelona’s Sagrada
Familia Cathedral.
M. Becerra et al. / Tourism Management 34 (2013) 71e79
- Number of Rooms. We controlled for hotel size using the total
number of rooms for the hotel. Just like firm size is usually
a significant variable in strategy research, hotel size may be
expected to affect pricing policy, for instance, as a result of
economies of scale.
- Age. We also controlled for the age of the hotel to account for
the possible greater pressure of older hotels to lower their
prices and possibly offer greater discounts.
Because two of these control variables were skewed (Number of
rooms and Age), we replicated the analysis using the log value of
these two last variables. The conclusions from the empirical analysis did not change when their log values were used.
3.3. Statistical analysis
We performed OLS regression analysis to test for the effect of
hotel Category and Chain, as measures of vertical and horizontal
differentiation respectively, on listed Room Prices and also on Price
Discounts. First, we included the control variables and fixed effects
for the 67 locations in the sample (Model 1), following Rigall-ITorrent and Fluvià (2007, 2011). Then, we added the independent
variables that gauge hotel differentiation and local competition
(Model 2). In the last step we included the hypothesized interactions. Because there was clear evidence of multicollinearity among
the interaction coefficients based on their variance inflation factors,
we standardized the four independent variables before computing
the interactions (Model 3). Thus, the differentiation and competition variables were standardized in models 2 and 3.
Though we controlled for location fixed effects that could drive
the relationship between differentiation and prices, e.g., through
demand shocks that affect different areas, there may still be an
issue of endogeneity of the Number of competitors with our
dependent variable Room prices. To explore this possibility, we
tested whether Number of competitors was endogenous through
a Hausman test (Durbin-Wu-Hausman test). An insignificant Chi
square of .23 (1 d.f., p-value ¼ .63) showed that indeed the number
of competitors is not endogenous.
We explored the robustness of our conclusions in two ways.
First, we replicated the analysis using structural equations
modeling (SEM), which allowed us to account for measurement
error in our latent construct of competition as well as to conduct
subsample analysis in order to check the invariance of the coefficients across groups of hotels with different levels of differentiation. Second, we used pricing data for the hotels that reported
separate prices for off-peak season and replicated the analysis over
this alternative set of prices and discounts. As we will discuss later
75
on, these analyses confirmed the conclusions from the OLS
regressions for peak season.
4. Results
4.1. Descriptive statistics
Table 1 shows the descriptive statistics and the correlation table
for the variables in the analysis. The average price of a double room
was 115 euros with a discount of 25% for single rooms. Based on the
zero-order correlations, hotels usually charge higher prices and
offer smaller discounts when they are newer, larger, and not close
to the beach. In line with the reputation of Spain as an affordable
destination for beaches and sun, hotels located near the beach seem
to attract the more price sensitive tourism. This result is in contrast
with the findings of previous literature (Aguiló, Alegre, & Riera,
2001; Espinet et al., 2003; Rigall-I-Torrent & Fluvià, 2007, 2011;
Rigall-I-Torrent et al., 2011) that report an increase in prices for
those hotels located just in front of the beach. We believe that these
different results are due to the fact that these studies analyze
a sample of hotels located exclusively in beach-and-sun Spanish
destinations (i.e., Costa Brava), whereas our sample comprises the
whole of Spain including beach-and sun locations, but also other
types of destinations like mountain resorts, big cities like Valencia
and Barcelona, and destinations with other landscape-cultural
attractions like Madrid or A Coruña. Being in front of the beach
may drive a price premium in beach-and sun destinations as reported in Espinet et al. (2003), though this beach premium may not
exist in other types of location, like Barcelona.
Table 2 shows average prices and discounts for hotels with
different categories and chain membership. On average, room prices increase from V60 for a one-star hotel to V280 for a five-star
hotel, while discounts for single room decrease from 29% to 16%
as the number of stars increases. Also, chain brand is associated
with higher prices and smaller discounts. Preliminary analysis of
variance based on ANOVA Bonferroni (not reported in the tables)
showed that the differences in Room Price and Price Discount for
the Chain and Category variables were highly significant and fully
in line with hypotheses 1 and 2.
4.2. Regression analysis
Table 3 shows the results for the regression analysis of Room
Price for the 1490 hotels in the sample using fixed effects for the 67
locations, while Table 4 repeats the same analysis for Price
Discounts. Model 1 includes only the control variables and the
dummies for 67 locations, which explain 49% of the variance in
prices and 23% of discounts. The key independent variables are
Table 1
Means, standard deviations, and correlations.
1. Room Price
2. Room Discount
3. Beach
4. Mountain
5. Special
6. Size
7. Age
8. Category
9. Chain
10. Geographical distance (km)
11. Number of competitors
y
p < .10.
*p < .05.
**p < .01.
N
Mean
St. Dev.
1
2
3
4
5
6
7
8
9
10
1490
1488
1490
1490
1490
1490
1490
1490
1490
1490
1490
115.61
.25
.32
.01
.31
96.51
11.90
2.84
.40
.62
21.18
60.85
.13
.47
.11
.46
88.72
10.81
.99
.49
.25
20.97
.32**
.16**
.09**
.01
.32**
.24**
.67**
.46**
.10**
.35**
.22**
.06*
.07*
.02
.19**
.27**
.29**
.00
.11**
.08**
.03
.31**
.14**
.00
.09**
.01
.02
.05
.08**
.02
.09**
-.10**
.06*
.08**
.13**
.02
.01
.09**
.01
.10**
.01
.47**
.36**
-.04
.18**
.26**
.19**
.01
.07**
.49**
.04
.22**
.02
.18**
.26**
76
M. Becerra et al. / Tourism Management 34 (2013) 71e79
Table 2
Mean prices and discounts by Hotel category and chain.
Category
1 star
2 stars
3 stars
4 stars
5 stars
Total chain
Table 4
Results of regression analysis for room discount with location fixed-effects.
Chain
(Yes ¼ 1)
Number
of hotels
Room
price (V)
Room
discount (%)
0
1
Total
0
1
Total
0
1
Total
0
1
Total
0
1
Total
0
1
165
9
174
280
41
321
335
254
589
103
277
380
4
22
26
887
603
1490
59.54
74.05
60.29
73.54
93.68
76.11
101.43
126.52
112.25
161.17
170.88
168.24
303.85
275.51
279.87
92.69
149.32
115.61
29.43
25.75
29.23
29.27
24.87
28.71
28.28
23.05
26.03
24.15
18.34
19.92
14.35
16.99
16.58
28.26
20.83
25.26
Overall mean
Model 1
Intercept
Beach
Mountain
Special
Size
Age
Competition
Number of competitors
Geographical distance
Differentiation
Category
Chain
Interactions
Number of Competitors Category
Number of Competitors Chain
Geographical Distance Category
Geographical Distance Chain
City dummies
R2
Adjusted R2
sε
(.04)
(.01)
(.04)
(.01)
(.00)
(.00)
Model 2
Model 3
.25**
.02*
.03
.02**
.00
.00**
.25**
.02y
.03
.02**
.00
.00**
(.04)
(.01)
(.04)
(.01)
(.00)
(.00)
(.04)
(.01)
(.04)
(.01)
(.00)
(.00)
.00 (.01)
.00 (.00)
.01 (.01)
.00 (.00)
.02** (.00)
.02** (.00)
.03** (.00)
.02** (.00)
.01*
.00
.01
.00
Yes
.23
.19
.11
1488
N
added in Model 2, which result in a significant increase in R2 of .24
for prices and .05 for discounts.
There is strong evidence in favor of hypotheses 1 and 2 in Table 3
for room prices and Table 4 for price discounts. In favor of H1a, the
number of stars has a large positive effect on Room Prices
(b ¼ 36.43); similarly for H2a, hotels that are part of a branded
chain are more expensive than independent hotels (b ¼ 4.63). Also
as we expected, Price Discount is reduced as hotels obtain higher
Category (b ¼ .02, supporting H1b) and Hotel Chains also charge
smaller discounts for single rooms (b ¼ .02, supporting H2b). All
of these coefficients are significant at 1% level. In addition to the
regression results reported in the tables, we should note that Hotel
Category has the largest effect on pricing policy, with a partial R2 of
.412 for Room Prices (.013 for Hotel Chain) and .025 for Price
Discounts (.020 for Hotel Chain).
Each of the interactions of Hotel Category and Chain with the
two measures of local competition is included in Model 3. The
results provide only partial support for hypothesis 3. More
.25**
.02y
.02
.02**
.00**
.00**
Yes
.28
.24
.11
1488
(.01)
(.00)
(.00)
(.00)
Yes
.28
.24
.11
1488
y
p < .10; *p < .05; **p < .01.
Standard errors in parentheses.
precisely, there is clear empirical evidence that greater competition
measured by the number of competitors makes the effect of Hotel
Category on both Room Prices and Price Discounts significantly
stronger. This is shown by the interaction coefficients in Model 3 in
Tables 3and 4. Hotels with more stars charge higher prices relative
to lower category hotels when the number of competitors is larger
(b ¼ 12.39, p-value < .01, Table 3) and they also provide relatively
smaller discounts in those circumstances (b ¼ .01, p-value < .05,
Table 4). However, competition interactions with Hotel Chain were
usually insignificant. The only significant interaction for the Chain
dummy variable (b ¼ 2.11, p-value < .05, Table 3) indicates that
hotels that are part of a branded chain charge significantly higher
prices when the number of direct competitors increases, which is in
line with hypothesis 3. Yet, the two interactions between competition (i.e., number of competitors and geographical distance) and
Table 3
Results of regression analysis for room Price with location fixed-effects.
Model 1
Intercept
Beach
Mountain
Special
Size
Age
Competition
Number of competitors
Geographical distance
Differentiation
Category
Chain
Interactions
Number of Competitors Category
Number of Competitors Chain
Geographical Distance Category
Geographical Distance Chain
City dummies
R2
Adjusted R2
sε
N
y
p < .10; *p < .05; **p < .01 Standard errors in parentheses.
120.20**
3.99
5.40
5.64*
.27**
1.08**
Model 2
(14.28)
(4.54)
(15.71)
(2.76)
(.02)
(.11)
113.62**
2.80
8.31
2.37
.03*
.33**
Model 3
(10.59)
(3.35)
(11.53)
(2.03)
(.01)
(.09)
110.81**
.28
7.63
2.83
.03*
.33**
10.02** (1.67)
1.08 (.98)
17.48** (1.77)
1.04 (.95)
36.43** (1.16)
4.63** (1.06)
42.38** (1.26)
4.41** (1.04)
12.39**
2.11*
1.15
1.62
Yes
.49
.47
43.17
1490
(10.24)
(3.26)
(11.13)
(1.96)
(.01)
(.08)
Yes
.73
.71
31.65
1490
Yes
.75
.73
30.49
1490
(1.56)
(.95)
(1.02)
(1.03)
M. Becerra et al. / Tourism Management 34 (2013) 71e79
chain are not significant in the regressions using discounts as
dependent variable in Table 4.
Overall, the results for vertical and horizontal differentiation
paint a clear picture regarding the key relevance of Category in
protecting hotels from reducing prices as competition increases.
When we examine the size of the standardized coefficients and the
partial r2 of the independent variables, the main effect of Hotel
Category is the key driver of prices and discounts, which becomes
even greater when the interactions with competition are considered. The expected effect of Hotel Category on Room Prices and
Discounts becomes significantly stronger as competition increases.
In contrast, Hotel Chain has a significant (though relatively smaller)
main effect on Room Prices and Discounts as hypothesis 2 claimed,
but such an effect is not moderated by the extent of competition
when using discounts as dependent variable.
4.3. Robustness tests
We replicated the entire analysis using SEM, which provided
very similar results. The baseline model and the key results are
depicted in Fig. 2. In model A, all the exogenous variables and the
Competition latent variable determine both Room Price and
Discount, whose errors are allowed to correlate. It should be noted
that this correlation is insignificant in all models, so that the
exogenous variables fully explain the observed correlation between
both. As usual in SEM, all exogenous variables and the Competition
77
latent variable are allowed to correlate freely. For identification
purposes of the Competition latent variable, two parameters are
fixed to 1. All variables in the model were centered around their
means for each location to obtain equivalent results to using location fixed effects, thus avoiding the inclusion of a large set of 67
dummy variables in the model.
The key results for Model A are shown in the first column below
Fig. 2. In line with H1and H2, the coefficients for the two types of
differentiation, Category and Chain, are significant and positively
associated with Price, but negatively with Discount. Note also that
the Competition latent variable is significant and negatively
correlated with Prices, though it is insignificant for Discounts.
The high CFI (.999) and low RMSEA (.008) indicate a very good fit.
Despite the large sample size, this model provides an insignificant
Chi-squared with 9 degrees of freedom.
To test the interactions of Competition with Category in Model B
using group analysis, the Category variable was dropped from
model A and the invariance of the Competition variable coefficient
was tested across the two groups, 1 and 2 star hotels vs. 4 and 5
hotels, thus dropping the 3-star hotels from the dataset. This model
still provides a good fit (CFI of .972 and RMSEA of .038), though
slightly smaller than Model A. Note that an increase in competition
results in lower prices for low category hotels (b ¼ 1.62, pvalue < .01), but an increase in prices for high quality hotels
(b ¼ 1.17, p-value < .01). This is fully consistent with the literature
on agglomeration effects (Canina, Enz, & Harrison, 2005). The test
KEY RESULTS
Model A
Price <=
Category
Chain
Competition
Discount <=
Category
Chain
Competition
Chi-2
CFI
RMSEA
1-2 stars
Model B
4-5 stars
Invariance
Independent
36.65**
9.30**
-.48**
-4.51
-1.62**
-27.24**
1.17**
-11.61**
119.62**
34.04**
--.31**
41.91**
--.55**
-.02**
-.04**
.00
9.90
.999
.008
--.04**
-.00
-1.42
.05
-.02**
-.00
-.03**
--.00
19.70
.999
.011
-.06**
-.00
29.96*
.972
.038
Model C
Chain
Fig. 2. Structural equations analysis baseline Graphical Representation (Model A).
Invariance
9.67**
-2.11
.47
-2.15
78
M. Becerra et al. / Tourism Management 34 (2013) 71e79
for group invariance between the two coefficients is highly significant (119.62, p-value < .01), so that differentiation indeed protects
from the pressure to reduce prices as competition increases, thus
providing support for hypothesis 3.
Model C shows the group comparison of independent vs.
chain hotels. In contrast to the results above, we can see that
Competition has a similar negative effect on prices (.31
and .55, both significant at p-value < .01) for both groups,
independent and chain hotels, so that there is no evidence of
interaction between Chain and Competition (2.11, insignificant
invariance across the coefficients in the two groups). We should
also note that the coefficients for Competition and their invariance across groups were insignificant for Discount in both
Models B and C. In sum, there is no support for hypothesis 3
regarding the moderating effect of competition on the chain/
pricing policy relationship.
All the OLS and SEM analyses reported so far were based on
prices and discounts during peak season for year 2005. We also
tested the robustness of the conclusions using data for off-peak
season. In many locations there is clearly a low season during
which the nature of competition changes, but less so in big cities
like Madrid and Barcelona. Many hotels do not report prices for low
season and they may even close, particularly some beach-and
mountain (skiing) hotels. Thus, the sample was reduced to 973
hotels during off-peak season. Similar results, though slightly
weaker, were obtained from this analysis. The coefficient for Hotel
Category was positive and significant for Room Price, though
insignificant for Hotel Chain, and both coefficients Category and
Chain were significant and negatively associated with Price
Discounts, as claimed by hypotheses 1 a/b and 2 a/b. Regarding the
moderating effect of competition, there was also partial evidence in
favor of hypotheses 3 with respect to Hotel Category, though mostly
insignificant results for Hotel Chain.
In summary, using an alternative method to test the hypothesized main effects and interactions and using also an alternative
dataset during off-peak season, we reached very similar conclusions to the main OLS analysis of prices and discounts during peak
season. Namely, there was clear evidence that both types of
differentiation, Hotel Category and Chain, are associated with
greater prices and lower discounts, as H1 and H2 suggested.
However, as competition increases, only Hotel Category protects
relatively more from the pressure to cut prices and provide greater
discounts. Thus, an increase in competition reinforces the effect of
Category on pricing policy, but it moderates only mildly the effect of
Chain. It should be noted that the moderating effect of competition
is observable mostly for Number of competitors and not usually for
average Geographical distance.
5. Discussion
The results from this study confirm our expectations regarding
the relationship between differentiation, competition, and pricing
policy in the hospitality industry. Hotels use differentiation strategies to escape from the competitive pressure to reduce prices. The
empirical evidence regarding main effects was generally supportive
using different types of differentiation strategy (vertical and horizontal), measures of competition (number of direct competitors
and geographical distance), and pricing policy (listed prices and
discounts).
Our empirical analysis discovered that vertical differentiation
seems to be more important than horizontal differentiation in the
understanding of pricing policy. Differences in hotel category
explain a greater percentage of variance in both prices and
discounts than hotel chain, as evidenced by the higher partial r2
and standardized coefficient size. Furthermore, the interaction of
hotel category with competition reinforces its positive effect in
tempering competitive pressures over prices, while the competition*chain interaction was usually insignificant. Thus, vertical
differentiation seems to be a more effective way to cope with
increases in competition. It appears that hotels with generally
acknowledged superior quality are better prepared to deal with an
increase in competitive rivalry than other organizations whose
superiority is only perceived by a specific customer group.
Certainly, horizontal differentiation that attracts a specific
customer segment is an adequate way to reduce competitive
pressure on prices, but being generally perceived as providing the
best service is more effective.
Based on our results, the managerial implications are straightforward. Differentiation indeed isolates companies from competitive pressures, but being better seems to be preferable to just being
different, though vertical differentiation may trigger a strategic race
to be the best that only a few hotels can win. Thus, increasing the
objective quality of the services provided by a hotel, measured in
our study by the number of stars, protects more from the pressure
to reduce prices than mere membership in a hotel chain.
6. Limitations and future research
Our paper is only a modest contribution to the relatively scarce
empirical literature on differentiation strategies and competition.
We explored two types of differentiation that are critical for hotel
management, such as overall quality and chain brand, but other
sources of differentiation should be studied as well to better
understand how hotels can deal with an increase in competition.
In addition, our empirical results are exclusively based on listed
prices. Recent research has found that some particular service
companies, as transportation and information technology, use
different pricing policies -like negotiated pricing- along with list
prices (Indounas, 2009). Though Avlonitis and Indounas (2007)
report that list pricing is the pricing policy most commonly used
by service companies, future research should investigate whether
our reported linkage between prices, discounts, and differentiation strategies hold with other aspects of pricing policy. For
instance, in the same hospitality industry both yield management
and negotiated pricing are prevalent practices and it could well be
the case that the effect of differentiation on these pricing policies
might differ from the relationships we have uncovered in this
paper.
Methodologically, our results are robust to the use of distinct
econometric techniques, including ordinary least squares and
structural equation modeling. However, we have used a single
cross section of the data and thus we are unable to estimate
specifications with individual firm fixed effects. This means that
we cannot completely rule out that our results may be partially
driven by unobserved firm characteristics correlated both with
differentiation strategies and pricing policies, though we have
controlled for any location effects. Future research should confirm
our findings in a panel data setting with estimations using firm
fixed effects.
Finally, future research may look at the differences in financial
performance between hotels with different levels of quality and
local competition, and whether hotels with generally acknowledged higher-quality products have been able to cope better with
the most recent economic crisis than less differentiated hotels.
Acknowledgment
We would like to thank the Spanish Ministerio de Ciencia e
Innovación for the funding received for project ECO2009-10891.
M. Becerra et al. / Tourism Management 34 (2013) 71e79
Appendix A. Supplementary material
Supplementary data related to this article can be found online at
doi:10.1016/j.tourman.2012.03.014
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